| Automation technology |
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| Teaching-learning-based optimization algorithm with hybrid learning strategy |
| BI Xiao-jun, WANG Jia-hui |
| College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China |
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Abstract An improved teaching-learning-based optimization (TLBO) algorithm based on hybrid learning strategies and disturbance was proposed to improve the searching functions of the algorithm and solve the problem of being easy to fall into local optima. The mutation strategy of differential evolution algorithm was merged into the learning part of the algorithm, and a hybrid learning strategy was propased to improve the learning ability of students in the later learning as well as the convergence performance of the algorithm. A new disturbance strategy was constructed in the late stage to reduce the possibility of trapping into local optima and ensure global optimality. Experimental results based on the standard test function demonstrate that the proposed algorithm can effectively increase the convergence speed and accuracy and significantly advance the optimization compared with the current similar four kinds of algorithms with excellent performance.
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Published: 01 May 2017
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Cite this article:
BI Xiao-jun, WANG Jia-hui. Teaching-learning-based optimization algorithm with hybrid learning strategy. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(5): 1024-1031.
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基于混合学习策略的教与学优化算法
为了提高教与学优化算法(TLBO)的搜索能力,解决算法易陷入局部最优的问题,提出基于混合学习策略和扰动的教与学优化算法.在教与学算法的学阶段融合差分进化算法变异策略,提出混合学习策略,使学员在学习后期具有更好的学习能力,提高算法的收敛性能；在算法后期提出新的扰动策略,减小学员在算法后期陷入局部最优的可能,保证算法全局最优性.基于标准测试函数的实验结果表明,相比于目前性能优异的同类4种算法,改进算法可有效提高算法的收敛速度和收敛精度,优化性能明显提高.
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